MP04 - Just the Fact(-Check)s, Ma’am!

Author

Tova Hirschhorn

Published

December 5, 2025

I. Executive Summary

On August 01, 2025, President Donald Trump fired Dr. Erika McEntarfer, Commissioner of the Bureau of Labor Statistics (BLS), stating his concerns about the size and direction of recent revisions to the monthly Current Employment Statistics (CES) “job numbers”.

The dismissal sparked bipartisan criticism from economists who warned that politicizing the Bureau of Labor Statistics could undermine the long-standing trust in federal statistics. Supporters of the agency noted that revisions are normal, expected, and built into the CES methodology.

Mini-Project 04 evaluates these competing claims, by analyzing data of CES revision patterns spanning nearly five decades. This is a nonpartisan, evidence-based analysis that recognizes both the methodological complexities and the important role that revisions play in improving the accuracy of federal labor statistics.

II. Data Acquisition and Preparation

This analysis draws on two primary data sources from the US Bureau of Labor Statistics:

  1. Final estimates of total nonfarm payroll employment
  2. Month-to-month revisions to those estimates

To construct the dataset of final CES employment levels, the “Total Nonfarm Payroll” series was programmatically retrieved using httr2 and rvest packages. The data were extracted from the BLS Data Finder, parsed from HTML, cleaned, and reshaped into a consistent monthly time series beginning in 1979.

Similarly, CES revision data was retrieved from a static BLS webpage using httr2 and rvest, then parsed, cleaned, and combined across years. For each month, revisions were calculated as the difference between the original and final published estimates.

Together, these steps produced a unified, fully automated dataset of CES employment levels and revisions covering January 1979 through June 2025.

IV. Statistical Analysis of CES Revisions

TEST #1:

IS THE AVERAGE REVISION DIFFERENT FROM ZERO?

A one-sample t-test shows that, on average, CES revisions increase initial employment estimates by roughly 11,500 jobs per month. The 95% confidence interval (4,570 to 18,530) and the small p-value (0.001) indicate that this result is statistically significant. Although this suggests a slight upward bias in initial CES reports, the magnitude of the revisions is small relative to total employment.

Code 
#Task 4- Statistical Inference
#Q3- Is the average revision significantly different from zero?
#Performing t-test
# Perform t-test
t_test_result <- t.test(ces_combined$revision, mu = 0)

# Create table from t-test results
ttest_df <- data.frame(
  Statistic = c("Mean", "Lower 95% CI", "Upper 95% CI", "t-value", "df", "p-value"),
  Value = c(
    round(t_test_result$estimate, 2),
    round(t_test_result$conf.int[1], 2),
    round(t_test_result$conf.int[2], 2),
    round(t_test_result$statistic, 3),
    round(t_test_result$parameter, 0),
    signif(t_test_result$p.value, 3)
  )
)

# Display table
kable(ttest_df, caption = "One-sample t-test results for CES revisions", align = "c") %>%
  kable_styling(full_width = FALSE, position = "center", bootstrap_options = c("striped", "hover", "condensed"))
One-sample t-test results for CES revisions
Statistic Value
Mean 11.50000
Lower 95% CI 4.57000
Upper 95% CI 18.43000
t-value 3.25900
df 557.00000
p-value 0.00118

TEST #2:

HAS THE AVERAGE REVISION INCREASED POST-2020?

A two-sample t-test comparing average CES revisions before and after 2020 indicates that the mean revision post-2020 is higher (~13,000) than pre-2020 (450). However, the large p-value (0.758) and wide confidence interval (-42,250 to Inf) show that this difference is not statistically significant. Overall, there is no strong evidence that CES revisions have increased in the post-2020 period. on average, CES revisions increase initial employment estimates by roughly 11,500 jobs per month.

Code 
# Task 4- Q4 - Prepare table for display
ttest_period_df <- data.frame(
  Statistic = c("Mean (Pre-2020)", "Mean (Post-2020)", "t-value", "df", "p-value",
                "Lower 95% CI", "Upper 95% CI"),
  Value = c(
    round(t_test_period$estimate[1], 2),
    round(t_test_period$estimate[2], 2),
    round(t_test_period$statistic, 3),
    round(t_test_period$parameter, 0),
    signif(t_test_period$p.value, 3),
    round(t_test_period$conf.int[1], 2),
    round(t_test_period$conf.int[2], 2)
  )
)

# Display table with kable
kable(ttest_period_df, caption = "Two-sample t-test: Average CES Revisions Pre-2020 vs Post-2020", align = "c") %>%
  kable_styling(full_width = FALSE, position = "center", bootstrap_options = c("striped", "hover", "condensed"))
Two-sample t-test: Average CES Revisions Pre-2020 vs Post-2020
Statistic Value
Mean (Pre-2020) 0.450
Mean (Post-2020) 12.980
t-value -0.702
df 70.000
p-value 0.758
Lower 95% CI -42.250
Upper 95% CI Inf

V. Fact Checks: What the Data Reveals About CES Revisions

CLAIM #1:

“When the data are unreliable, when they keep being revised all over the place, then there are going to be people that wonder if there’s a partisan pattern in the data.”

— Kevin Hassett, Director, National Economic Council (under President Trump) Source: The Hill, Aug 03, 2025

The plot below, which tracks the share of negative revisions, supports the stability of revisions over time. The proportion of downward revisions pre-2000 and post-2000 is nearly unchanged, which suggests the pattern of CES adjustments has been highly consistent for decades.

A separate two-sample test performed earlier in the analysis compared the fraction of negative CES revisions before and after 2020 to assess more recent changes. The test statistic (X2 = 0.608) and large p-value (0.782) indicate no meaningful statistical difference, while the confidence interval (-1.000 to 0.109) confirms that any gap is minimal. This reinforces the broader conclusion that CES revisions have remained stable over time.

Conclusion: These findings provide no evidence that revisions have suddenly become erratic or politically motivated, directly undermining the claim that CES revisions “are all over the place.”

Politifact Truth-O-Meter Rating: FALSE

CLAIM #2:

“CES revisions happen every month and always have. They are a sign of statistical transparency, not failure”.

— Jason Furman, Former Chair, Council of Economic Advisers
Source: X/Twitter, Aug 01, 2025

Monthly CES revisions occur consistently, but their size is generally small and stable over time. Earlier in the analysis, the “Evolution of US Total Nonfarm Payrolls” plot showed that these routine adjustments do not disrupt the long-run upward trend in employment.

The histogram of monthly CES revisions below reinforces this pattern: most revisions are modest and clustered near zero. This aligns with the one-sample t-test performed earlier, which indicate that revisions increase initial employment estimates by 11,500 jobs per month, with a 95% confidence interval ranging from 4,570 to 18,430, and a p-value of 0.001.

Code 
# Task 4 Q3.-Visualize distribution
ggplot(ces_combined, aes(x = revision)) +
  geom_histogram(binwidth = 50, fill = "steelblue", color = "white", alpha = 0.8) +
  geom_vline(xintercept = 0, color = "red", linetype = "dashed", size = 0.8) +
  scale_x_continuous(breaks = seq(-300, 300, by = 50)) +
  coord_cartesian(xlim = c(-300, 300)) +
  labs(
    title = "Distribution of US Employment Revisions",
    subtitle = "Red dashed line indicates zero revision",
    x = "Monthly CES Revision (Thousands)",
    y = "Frequency"
  ) +
  theme_minimal(base_size = 9) +
  theme(
    plot.title = element_text(hjust = 0.5, size = 11, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 9),
    axis.title = element_text(size = 9),
    axis.text = element_text(size = 8)
  )

Conclusion: This distribution confirms that these revisions are a normal and expected part of the CES process, supporting Furman’s claim that revisions reflect statistical transparency rather than reporting failure.

Politifact Truth-O-Meter Rating: TRUE

VI. Behind the Numbers: How Computationally-Intensive Statistics Work

1. What is Computationally-Intensive Statistical Inference: A Non-Technical Explanation

Computationally intensive statistical inference is a modern approach that uses powerful computers to answer statistical questions when traditional formulas are too limited. Instead of relying on neat mathematical equations, these methods can handle large datasets, complex patterns, and situations where classical approaches breaks down.

Rather than assuming the data follows a perfect mathematical distribution, the computer repeatedly simulates new versions of the data. Techniques such as bootstrap, permutation tests, and Markov Chain Monte Carlo allow statisticans to approximate what would happen if we could “re-run” the world thousands of times under slightly different conditions.

By comparing our real result to the results from these simulated “what-if” worlds, we can see whether what we observed is unusual or just typical random variation. If a result rarely appears in the simulations, then it is unlikely to have occurred by chance in the real world.

2. How It Works: A Visual Representation

The flowchart below illustrates the step-by-step process behind computationally intensive statistical inference.

flowchart TD
%% Node styling
style A fill:#f0f8ff,stroke:#333,stroke-width:1px
style B fill:#f0f8ff,stroke:#333,stroke-width:1px
style C fill:#f0f8ff,stroke:#333,stroke-width:1px
style D fill:#f0f8ff,stroke:#333,stroke-width:1px
style E fill:#f0f8ff,stroke:#333,stroke-width:1px
style F fill:#f0f8ff,stroke:#333,stroke-width:1px
style G fill:#f0f8ff,stroke:#333,stroke-width:1px
style H fill:#f0f8ff,stroke:#333,stroke-width:1px
style I fill:#f0f8ff,stroke:#333,stroke-width:1px
style J fill:#f0f8ff,stroke:#333,stroke-width:1px
style K fill:#f0f8ff,stroke:#333,stroke-width:1px

%% Flow with wrapped text
A[Start with observed data] --> B[Define the question]
B --> C[Pick a simulation method - e.g., permutation, bootstrap, MCMC]
C --> D[Generate many new datasets]
D --> E[Calculate the statistic for each dataset]
E --> F[Form the simulated distribution]
F --> G[Compare the simulated statistic]
G --> H{Is the observed value unusual?}
H -- No --> I[Result likely due to chance] --> K[Final conclusion]
H -- Yes --> J[Result unlikely due to chance] --> K

%% Optional: thicker arrows
linkStyle default stroke:#2980B9, stroke-width:2px;

VII. Conclusion

The comprehensive analysis of nearly five decades of CES data demonstrates that monthly employment revisions are normal, expected, and a relatively small part of the Bureau Labor Statistic’s reporting process.

Revisions have been consistent over time, with patterns pre- and post-2020 being nearly identical. Statistical tests confirm no significant changes, undermining claims that CES data is erratic or politically influenced.

Larger than average revisions generally correspond to unusual economic events, such recessions, seasonal transitions, or the COVID-19 pandemic. This highlights the responsiveness of CES methodology to real-world conditions.

Moreover, simulation-based inference confirms that observed patterns are typical and not random, reinforcing confidence in the reliability of CES data.

Policymakers, economists, and most importantly, the public, can continue to rely on the trustworthiness of CES data and its methodology in measuring US labor market trends.

This work ©2025 by Ghirschhorn was initially prepared as a Mini-Project for STA 9750 at Baruch College. More details about this course can be found at the course site and instructions for this assignment can be found at MP #04